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Found 18 papers, 11 papers with code
Data Integration for Toxic Comment Classification: Making More Than 40 Datasets Easily Accessible in One Unified Format
With the rise of research on toxic comment classification, more and more annotated datasets have been released.
Overview of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments
We present the GermEval 2021 shared task on the identification of toxic, engaging, and fact-claiming comments.
Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs
Here, a powerful LLM is prompted with a task description to generate labeled data that can be used to train a downstream NLP model.
Pseudo-Labels Are All You Need
Automatically estimating the complexity of texts for readers has a variety of applications, such as recommending texts with an appropriate complexity level to language learners or supporting the evaluation of text simplification approaches.
Semantic Answer Similarity for Evaluating Question Answering Models
To this end, we create an English and a German three-way annotated evaluation dataset containing pairs of answers along with human judgment of their semantic similarity, which we release along with an implementation of the SAS metric and the experiments.
Multi-modal Retrieval of Tables and Texts Using Tri-encoder Models
In this paper, we present an approach for retrieving both texts and tables relevant to a question by jointly encoding texts, tables and questions into a single vector space.
Multifaceted Domain-Specific Document Embeddings
Current document embeddings require large training corpora but fail to learn high-quality representations when confronted with a small number of domain-specific documents and rare terms.
GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval
A major challenge of research on non-English machine reading for question answering (QA) is the lack of annotated datasets.
PatentMatch: A Dataset for Matching Patent Claims & Prior Art
For these reasons, we address the computer-assisted search for prior art by creating a training dataset for supervised machine learning called PatentMatch.
A Dataset of Journalists' Interactions with Their Readership: When Should Article Authors Reply to Reader Comments?
Based on this data, we formulate the novel task of recommending reader comments to journalists that are worth reading or replying to, i. e., ranking comments in such a way that the top comments are most likely to require the journalists' reaction.
Bagging BERT Models for Robust Aggression Identification
In this paper, we describe such an ensemble system and present our submission to the shared tasks on aggression identification 2020 (team name: Julian).
Offensive Language Detection Explained
However, even with machine-learned models achieving better classification accuracy than human experts, there is still a reason why human moderators are preferred.
Top Comment or Flop Comment? Predicting and Explaining User Engagement in Online News Discussions
In this paper, we systematically analyze user engagement in the form of the upvotes and replies that a comment receives.
My Approach = Your Apparatus? Entropy-Based Topic Modeling on Multiple Domain-Specific Text Collections
Comparative text mining extends from genre analysis and political bias detection to the revelation of cultural and geographic differences, through to the search for prior art across patents and scientific papers.
Challenges for Toxic Comment Classification: An In-Depth Error Analysis
Toxic comment classification has become an active research field with many recently proposed approaches.
Aggression Identification Using Deep Learning and Data Augmentation
Social media platforms allow users to share and discuss their opinions online.
Delete or not Delete? Semi-Automatic Comment Moderation for the Newsroom
Comment sections of online news providers have enabled millions to share and discuss their opinions on news topics.
Prediction for the Newsroom: Which Articles Will Get the Most Comments?
The overwhelming success of the Web and mobile technologies has enabled millions to share their opinions publicly at any time.
